The Research of Ortho-Rectification to QuickBird Image with more Mountains Based on ERDAS10.0

2014 ◽  
Vol 571-572 ◽  
pp. 772-776 ◽  
Author(s):  
Cong Li ◽  
Qiang Wang ◽  
Meng Wang ◽  
Jia Jie Cui
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Orthogonal Projection ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Positioning Accuracy ◽  
Remote Sensing Technology ◽  
Sensor Model ◽  
Sensing Technology

In recent years, the application of high resolution remote sensing images has become more and more widely with the development of Remote Sensing technology. QuickBird satellite image is the more commercial used high resolution remote sensing image, but due to its technical confidentiality, high-resolution satellite generally does not provide rigorous sensor model. This paper uses ERDAS10.0 to orthographic check the QuickBird image by the method of orthorectification, introduces the method and procedure of the orthogonal projection like drawing, analysis the positioning accuracy.

Remote sensing image quality evaluation based on deep learning

2021 ◽  
pp. 1-9
Author(s):  
Tong Wang ◽  
Hemeng Yang ◽  
Ling Zhu ◽  
Yazhou Fan ◽  
Xue Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Quality Evaluation ◽  
Remote Sensing Image ◽  
Human Vision ◽  
Remote Sensing Images ◽  
Image Quality Evaluation ◽  
Remote Sensing Technology ◽  
Sensing Technology

Remote sensing technology is an effective tool for sensing the earth’s surface. With the continuous improvement of remote sensing technology, remote sensing detectors can obtain more spectral and spatial information, including clear feature contours, complex texture features and spatial layout rules. This information was detected in mineral resources. Surface substance identification, water pollution information monitoring and many other aspects have played an important role. The coding algorithm and defects, storage algorithm and interference from atmospheric cloud radiation information during the imaging process lead to varying degrees of distortion and deterioration of remote sensing images during imaging, transmission and storage. This makes it difficult to process, analyze and apply remote sensing images. Therefore, the design of a reasonable remote sensing image quality evaluation method is not only conducive to the remote sensing image quality evaluation in the real-time processing system of remote sensing image, but also conducive to the optimization of remote sensing image system and image processing algorithm. The application is worthwhile. In this paper, the deteriorating features of remote sensing images will change the statistical distribution. We propose a method for evaluating the quality of remote sensing images in depth learning. Feature learning and blurring as well as noise intensity classification for image remote sensing using convolutional neural network are carried out. The evaluation model is modified by masking effect and perceptual weighting factor, and the quality evaluation results of remote sensing images are obtained according to human vision. The research shows that this method can effectively solve the problem of removing and evaluating the noise of remote sensing image, and can effectively and accurately evaluate the quality of remote sensing image. It is also consistent with subjective assessment and human perception.

scholarly journals THE TYPHOON DISASTER ANALYSIS EMERGENCY RESPONSE SYSTEM BASED ON UAV REMOTE SENSING TECHNOLOGY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 959-965
Author(s):  
Q. J. Chen ◽  
Y. R. He ◽  
T. T. He ◽  
W. J. Fu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Visual Analysis ◽  
Satellite Image ◽  
Image Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Disaster Information ◽  
Satellite Image Data ◽  
Typhoon Disaster

Abstract. The satellite image data has some shortcomings such as poor timeless, incomplete disaster information and so on in the typhoon disaster analysis. Compared with the satellite image data, unmanned aerial vehicle (UAV) remote sensing technology has the characteristics of flexibility, convenience, high resolution and so on. It plays a great role in the aspect of obtaining the images and systematically analyze the disaster data. This research based on UAV technology to obtain the high resolution image data and complied the disaster thematic maps after interpretation, as well as determining the data model. Subsequently, determining the system used Html, Javascript and CSS to build the system framework. Combining with Postgre SQL database, Leaflet map module and Echarts diagram and other technologies to perform the feasibility analysis and the detailed design of the integrated system. Finally, it could accurately and comprehensively obtain the system’s disaster monitoring, the typhoon track display, the diagram statistics and visual analysis of the data processing, as well it could deeply analysis and management for the disaster information and assessment. The application shows that this system could provide the information support for future emergency rescue, which is of great significance for the monitoring and preventing the occurrence natural disasters in the future.

A CBIR System for Hyperspectral Remote Sensing Images Using Endmember Extraction

2017 ◽  
Vol 31 (04) ◽  
pp. 1752001 ◽  
Author(s):  
Jing Zhang ◽  
Qianlan Zhou ◽  
Li Zhuo ◽  
Wenhao Geng ◽  
Suyu Wang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Hyperspectral Remote Sensing ◽  
Superior Performance ◽  
Endmember Extraction ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Hyperspectral Remote Sensing Images ◽  
Hyperspectral Remote Sensing Image

With the rapid development of remote sensing technology, searching the similar image is a challenge for hyperspectral remote sensing image processing. Meanwhile, the dramatic growth in the amount of hyperspectral remote sensing data has stimulated considerable research on content-based image retrieval (CBIR) in the field of remote sensing technology. Although many CBIR systems have been developed, few studies focused on the hyperspectral remote sensing images. A CBIR system for hyperspectral remote sensing image using endmember extraction is proposed in this paper. The main contributions of our method are that: (1) the endmembers as the spectral features are extracted from hyperspectral remote sensing image by improved automatic pixel purity index (APPI) algorithm; (2) the spectral information divergence and spectral angle match (SID–SAM) mixed measure method is utilized as a similarity measurement between hyperspectral remote sensing images. At last, the images are ranked with descending and the top-[Formula: see text] retrieved images are returned. The experimental results on NASA datasets show that our system can yield a superior performance.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

Remote Sensing Image Mosaic Algorithm

2012 ◽  
Vol 500 ◽  
pp. 716-721
Author(s):  
Yi Ding Wang ◽  
Shuai Qin
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Hot Spot ◽  
Remote Sensing Image ◽  
Processing Algorithm ◽  
Geometric Features ◽  
Image Mosaic ◽  
Post Processing ◽  
Remote Sensing Images ◽  
Image Pixels

In the field of remote sensing, the acquirement of higher resolution of remote sensing images has become a hot spot issue with widely use of high resolution of remote sensing images. This paper focus on the characteristics of high resolution remote sensing images, on the basis of fully considerate of the correlation between geometric features and image pixels, bring forward a fusion of image mosaic processing algorithm. With this algorithm, the surface features can be well preserved after the processing of mosaic the remote sensing images, and the overlapping area can transit naturally, it will be better for the post-processing, analysis and application.

scholarly journals MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4528
Author(s):  
Xin Yang ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Yunqing Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Image Features ◽  
Remote Sensing Images ◽  
The Rich ◽  
The Difference ◽  
Basic Network ◽  
And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

The Study on Environmental Evolved Characteristic of Sandy Coast

2012 ◽  
Vol 226-228 ◽  
pp. 1170-1173
Author(s):  
Qi Peng Zhang ◽  
Xiao Qing Han ◽  
Jing Li ◽  
Jing Jing Zhao ◽  
Wei Biao Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Bohai Sea ◽  
Hebei Province ◽  
Coastal Current ◽  
Remote Sensing Images ◽  
Remote Sensing Technology ◽  
The North ◽  
Sensing Technology ◽  
Sandy Coast

In order to study the evolved characteristic of sandy coast in Hebei Province, the paper analyzed costal information by Remote Sensing technology from landform maps and remote sensing images from 1956 to 2007. It studied the evolvement characteristics and the reasons of sandy coast deeply. And it also analyzed the evolvement infections to the nearby coast of the sandy engineering. The results showed that the characteristic was erosion condition in sandy coast. There were several different evolved processing in different area from 1959 to 2007. In the region between Daihe River and Tazigou, the highest erosion speed was 3.45 m/a by the coastal current and wave between Daihe River and Yanghe River. The section was deposited into the ocean with the speed of 1.29 m/a by the cultivation ponds building in Bohai Sea farmland between the Yanghe River and Dapuhe River. In the region between Tazigou and Langwokou River, the beach had been eroded about 373 m with the speed of 13.32 m/a by 2007. And the section was eroded offshore more serious with the distance of 610 m and the speed of 21.79 m/a from the north of Luanhe River.In the region between Langwokou River and Daqinghe River, the average erosion distance was about 370 m with the speed of 13.21 m/a in Shegang sandbar. And it was eroded back to mainland about 164 m with the speed of 8.20 m/a. And it was about 504m with the speed of 18.00 m/a.

scholarly journals STUDY OF DUST POLLUTION DETECTING ABILITY IN URBAN AREAS BY REMOTE SENSING TECHNOLOGY TO SUPPORT AIR ENVIRONMENT OBSERVATION

2012 ◽  
Vol 15 (4) ◽  
pp. 33-47
Author(s):  
Van Thi Tran ◽  
Binh Thi Trinh ◽  
Bao Duong Xuan Ha
Keyword(s):  
Remote Sensing ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Satellite Image ◽  
Regression Function ◽  
Statistical Correlation ◽  
Step Test ◽  
Residential Areas ◽  
Remote Sensing Technology ◽  
Sensing Technology

This paper presents the approach towards application of remote sensing technology to monitor the air environemnt. Specific inital research is findings PM10 dust from SPOT 5 satellite image. The calculation based on reflectance value on remote sensing satellite images. The main method is to calculate statistical correlation regression between the PM10 concentration from ground station observations and reflectance value on each image band and the main components of satellite imagery in 2003 to find the best regression function, applied then to images 2011 where its radiance value was relatively normalized under atmospheric, geometric, environmental conditions of image 2003. The results showed the best correlation in nonlinear regression case. Spatial distribution of PM10 concentrations > 200μg/m3 found on most main roads, industrial parks and residential areas. This study is a first step test, but the results have demonstrated that satellite imagery can be used as a useful, effective tool, to monitor air environment in cities.

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